docstr: This model uses the ``GaussianProcessRegressor`` of scikit-learn to fit a Gaussian Process to the
  supplied TimeSeries. This can then be used to obtain samples from the
  Gaussian Process at the times of the TimeSeries.
  
  It can for instance be used to fill in missing (NaN) values from a TimeSeries.
  
  Parameters
  ----------
  kernel : sklearn.gaussian_process.kernels.Kernel, default: None
      The kernel specifying the covariance function of the Gaussian Process. If None is passed,
      the default in scikit-learn is used. Note that the kernel hyperparameters are optimized
      during fitting unless the bounds are marked as 'fixed'.
  **kwargs
      Additional keyword arguments passed to ``sklearn.gaussian_process.GaussianProcessRegressor``.
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code: def __init__(self, kernel: Optional[Kernel] = None, **kwargs):
      """
      This model uses the ``GaussianProcessRegressor`` of scikit-learn to fit a Gaussian Process to the
      supplied TimeSeries. This can then be used to obtain samples from the
      Gaussian Process at the times of the TimeSeries.
  
      It can for instance be used to fill in missing (NaN) values from a TimeSeries.
  
      Parameters
      ----------
      kernel : sklearn.gaussian_process.kernels.Kernel, default: None
          The kernel specifying the covariance function of the Gaussian Process. If None is passed,
          the default in scikit-learn is used. Note that the kernel hyperparameters are optimized
          during fitting unless the bounds are marked as 'fixed'.
      **kwargs
          Additional keyword arguments passed to ``sklearn.gaussian_process.GaussianProcessRegressor``.
      """
      super().__init__()
      self.model = GaussianProcessRegressor(kernel=kernel, **kwargs)
  
file_path: darts/darts/models/filtering/gaussian_process_filter.py
imports: from typing import Optional
  import numpy
  from sklearn.gaussian_process import GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import Kernel
  from darts.models.filtering.filtering_model import FilteringModel
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 1 to create a new GaussianProcessFilter object from inherited base classes: FilteringModel with example: obj = GaussianProcessFilter(kernel)